Time series Anomaly Detection (AD) plays a crucial role for web systems.
Various web systems rely on time series data to monitor and identify anomalies
in real time, as well as to initiate diagnosis and remediation procedures.
Variational Autoencoders (VAEs) have gained popularity in recent decades due to
their superior de-noising capabilities, which are useful for anomaly detection.
However, our study reveals that VAE-based methods face challenges in capturing
long-periodic heterogeneous patterns and detailed short-periodic trends
simultaneously. To address these challenges, we propose Frequency-enhanced
Conditional Variational Autoencoder (FCVAE), a novel unsupervised AD method for
univariate time series. To ensure an accurate AD, FCVAE exploits an innovative
approach to concurrently integrate both the global and local frequency features
into the condition of Conditional Variational Autoencoder (CVAE) to
significantly increase the accuracy of reconstructing the normal data. Together
with a carefully designed "target attention" mechanism, our approach allows the
model to pick the most useful information from the frequency domain for better
short-periodic trend construction. Our FCVAE has been evaluated on public
datasets and a large-scale cloud system, and the results demonstrate that it
outperforms state-of-the-art methods. This confirms the practical applicability
of our approach in addressing the limitations of current VAE-based anomaly
detection models.

我们提出了一种新的无监督异常检测方法 FCVAE，通过同时集成全局和局部频率特征，显著提高了正常数据的重构准确性，以解决基于 VAE 的方法在捕捉长周期异质模式和详细短周期趋势方面面临的挑战。通过精心设计的 “目标注意力” 机制，我们的方法能够从频域选择最有用的信息以更好地构建短周期趋势。在公开数据集和大规模云系统上对 FCVAE 进行了评估，结果表明它优于最先进的方法，验证了我们的方法在解决当前基于 VAE 的异常检测模型的局限性方面的实际适用性。